A weighted word2vec-attention long short-term memory(WWAL)emotion analysis model is proposed to overcome the shortcomings of traditional machine learning-based sentiment analysis algorithms of relying on manual establishment of emotional dictionary and manual intervention. The role of keywords in the comment text is highlighted. Word vectors based on word2vec are formed by introducing the term frequency-inverse document frequency(TFIDF)algorithm. An attention mechanism is introduced in the long short-term memory(LSTM)networks model. Experimental results on the standard dataset show the WWAL model is better than the traditional machine learning method in terms of precision,recall and F1 indicators.